Hidden location prediction using check-in patterns in location based social networks
- Submitting institution
-
Loughborough University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2454
- Type
- D - Journal article
- DOI
-
10.1007/s10115-018-1170-5
- Title of journal
- Knowledge and Information Systems
- Article number
- -
- First page
- 571
- Volume
- 57
- Issue
- 3
- ISSN
- 0219-1377
- Open access status
- Compliant
- Month of publication
- February
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 10
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper has impacted the state of the art on the analysis of data gaps in location based social networks, outperforming the hidden location leakage probability model. The associative location prediction model developed was evaluated against the industry standard Gowalla location based social network dataset, which contains over 6.4 million location check-ins.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -